Growing amount of urban traffic has led to excessive rise in the traffic accident statistics. It is common for drivers to ignore signs, especially the traffic signs displaying speed limits. These signs are provided to insure driving safety and comfort. In order to follow the signs, it is important that the driver is able to recognize the sign and accordingly take necessary measures.
Nowadays, various Advanced Driver Assistance Systems (ADAS) help drivers in the driving process by maintaining both vehicle and road safety. Traffic sign recognition is a component of Advanced Driver Assistance System that helps drivers by communicating information related to the traffic signs. Such systems assist drivers in following traffic rules. Existing traffic sign detection methods use different machine learning techniques. These techniques are computationally complex and require offline training Additionally, in such systems successful digit recognition depends on illumination i.e. on the lighting conditions as well as on the scale of the traffic signs. Moreover, based on these conditions the digits extracted from the traffic signs may be incomplete, thus, resulting in incorrect recognition.
Therefore, there is a need for a system that is independent of the machine learning techniques and which limits the aforementioned drawbacks.